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Individual Variability and Test-Retest Reliability Revealed by Ten Repeated Resting-State Brain Scans over One Month

Individual differences in mind and behavior are believed to reflect the functional variability of the human brain. Due to the lack of a large-scale longitudinal dataset, the full landscape of variability within and between individual functional connectomes is largely unknown. We collected 300 restin...

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Autores principales: Chen, Bing, Xu, Ting, Zhou, Changle, Wang, Luoyu, Yang, Ning, Wang, Ze, Dong, Hao-Ming, Yang, Zhi, Zang, Yu-Feng, Zuo, Xi-Nian, Weng, Xu-Chu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4694646/
https://www.ncbi.nlm.nih.gov/pubmed/26714192
http://dx.doi.org/10.1371/journal.pone.0144963
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author Chen, Bing
Xu, Ting
Zhou, Changle
Wang, Luoyu
Yang, Ning
Wang, Ze
Dong, Hao-Ming
Yang, Zhi
Zang, Yu-Feng
Zuo, Xi-Nian
Weng, Xu-Chu
author_facet Chen, Bing
Xu, Ting
Zhou, Changle
Wang, Luoyu
Yang, Ning
Wang, Ze
Dong, Hao-Ming
Yang, Zhi
Zang, Yu-Feng
Zuo, Xi-Nian
Weng, Xu-Chu
author_sort Chen, Bing
collection PubMed
description Individual differences in mind and behavior are believed to reflect the functional variability of the human brain. Due to the lack of a large-scale longitudinal dataset, the full landscape of variability within and between individual functional connectomes is largely unknown. We collected 300 resting-state functional magnetic resonance imaging (rfMRI) datasets from 30 healthy participants who were scanned every three days for one month. With these data, both intra- and inter-individual variability of six common rfMRI metrics, as well as their test-retest reliability, were estimated across multiple spatial scales. Global metrics were more dynamic than local regional metrics. Cognitive components involving working memory, inhibition, attention, language and related neural networks exhibited high intra-individual variability. In contrast, inter-individual variability demonstrated a more complex picture across the multiple scales of metrics. Limbic, default, frontoparietal and visual networks and their related cognitive components were more differentiable than somatomotor and attention networks across the participants. Analyzing both intra- and inter-individual variability revealed a set of high-resolution maps on test-retest reliability of the multi-scale connectomic metrics. These findings represent the first collection of individual differences in multi-scale and multi-metric characterization of the human functional connectomes in-vivo, serving as normal references for the field to guide the use of common functional metrics in rfMRI-based applications.
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spelling pubmed-46946462016-01-13 Individual Variability and Test-Retest Reliability Revealed by Ten Repeated Resting-State Brain Scans over One Month Chen, Bing Xu, Ting Zhou, Changle Wang, Luoyu Yang, Ning Wang, Ze Dong, Hao-Ming Yang, Zhi Zang, Yu-Feng Zuo, Xi-Nian Weng, Xu-Chu PLoS One Research Article Individual differences in mind and behavior are believed to reflect the functional variability of the human brain. Due to the lack of a large-scale longitudinal dataset, the full landscape of variability within and between individual functional connectomes is largely unknown. We collected 300 resting-state functional magnetic resonance imaging (rfMRI) datasets from 30 healthy participants who were scanned every three days for one month. With these data, both intra- and inter-individual variability of six common rfMRI metrics, as well as their test-retest reliability, were estimated across multiple spatial scales. Global metrics were more dynamic than local regional metrics. Cognitive components involving working memory, inhibition, attention, language and related neural networks exhibited high intra-individual variability. In contrast, inter-individual variability demonstrated a more complex picture across the multiple scales of metrics. Limbic, default, frontoparietal and visual networks and their related cognitive components were more differentiable than somatomotor and attention networks across the participants. Analyzing both intra- and inter-individual variability revealed a set of high-resolution maps on test-retest reliability of the multi-scale connectomic metrics. These findings represent the first collection of individual differences in multi-scale and multi-metric characterization of the human functional connectomes in-vivo, serving as normal references for the field to guide the use of common functional metrics in rfMRI-based applications. Public Library of Science 2015-12-29 /pmc/articles/PMC4694646/ /pubmed/26714192 http://dx.doi.org/10.1371/journal.pone.0144963 Text en © 2015 Chen et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Chen, Bing
Xu, Ting
Zhou, Changle
Wang, Luoyu
Yang, Ning
Wang, Ze
Dong, Hao-Ming
Yang, Zhi
Zang, Yu-Feng
Zuo, Xi-Nian
Weng, Xu-Chu
Individual Variability and Test-Retest Reliability Revealed by Ten Repeated Resting-State Brain Scans over One Month
title Individual Variability and Test-Retest Reliability Revealed by Ten Repeated Resting-State Brain Scans over One Month
title_full Individual Variability and Test-Retest Reliability Revealed by Ten Repeated Resting-State Brain Scans over One Month
title_fullStr Individual Variability and Test-Retest Reliability Revealed by Ten Repeated Resting-State Brain Scans over One Month
title_full_unstemmed Individual Variability and Test-Retest Reliability Revealed by Ten Repeated Resting-State Brain Scans over One Month
title_short Individual Variability and Test-Retest Reliability Revealed by Ten Repeated Resting-State Brain Scans over One Month
title_sort individual variability and test-retest reliability revealed by ten repeated resting-state brain scans over one month
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4694646/
https://www.ncbi.nlm.nih.gov/pubmed/26714192
http://dx.doi.org/10.1371/journal.pone.0144963
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